{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spamhmm-sparse-mixture-of-hidden-markov","title":"SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities","arxiv_id":"1904.00442","date":"2019-03-31","proceeding":null,"authors":["Diogo Pernes","Jaime S. Cardoso"],"abstract":"We propose a framework to model the distribution of sequential data coming\nfrom a set of entities connected in a graph with a known topology. The method\nis based on a mixture of shared hidden Markov models (HMMs), which are jointly\ntrained in order to exploit the knowledge of the graph structure and in such a\nway that the obtained mixtures tend to be sparse. Experiments in different\napplication domains demonstrate the effectiveness and versatility of the\nmethod.","url_abs":"http://arxiv.org/abs/1904.00442v1","url_pdf":"http://arxiv.org/pdf/1904.00442v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spamhmm-sparse-mixture-of-hidden-markov","repo_url":"https://github.com/dpernes/spamhmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}